Hongtao Zhang

Bio: Hongtao Zhang is an academic researcher from Beijing University of Posts and Telecommunications. The author has contributed to research in topics: Computer science & Handover. The author has an hindex of 10, co-authored 36 publications receiving 481 citations.

On the Capacity Comparison Between MIMO-NOMA and MIMO-OMA

Abstract: Non-orthogonal multiple access (NOMA) has been shown in the literature to have a better performance than OMA in terms of sum channel capacity; however, the capacity superiority of NOMA over OMA has been only proved for single antenna systems, and the proof for the capacity superiority of multiple-input multiple-output NOMA (MIMO-NOMA) over conventional MIMO-OMA has not been available yet. In this paper, we will provide our proof to demonstrate that the MIMO-NOMA is strictly better than MIMO-OMA in terms of sum channel capacity (except for the case where only one user is being communicated to), i.e., for any rate pair achieved by MIMO-OMA, there is a power split for which MIMO-NOMA can achieve rate pairs that are strictly larger. Based on this result, we prove that the MIMO-NOMA can also achieve a larger sum ergodic capacity than MIMO-OMA. Our analytical results are verified by simulations.

Mobility Prediction: A Survey on State-of-the-Art Schemes and Future Applications

Abstract: Recently, mobility has gathered tremendous interest as the users’ desire for consecutive connections and better quality of service has increased. An accurate prediction of user mobility in mobile networks provides efficient resource and handover management, which can avoid unacceptable degradation of the perceived quality. Therefore, mobility prediction in wireless networks is of great importance and many works have been dedicated to this issue. In this paper, the necessity of mobility prediction, together with its intrinsic characteristics in terms of movement predictability, prediction outputs, and performance metrics is discussed. Moreover, the learning perspective of solutions to mobility prediction has been studied. Specifically, an overview of the state-of-the-art approaches is provided, including Markov chain, hidden Markov model, artificial neural network, Bayesian network, and data mining based on different kinds of knowledge. At last, this paper also explores the open research challenges due to the advent of the fifth-generation mobile system and puts forward some potential trends in the near future.

Multi-Branch Deep Residual Learning for Clustering and Beamforming in User-Centric Network

Abstract: In existing works of deep learning-based resource allocation, the scalability degrades heavily with the increase of network complexity, which is due to their limited learning ability of shallow neural networks and insufficient knowledge of network. Nowadays, to address the growth of cell density, cooperative beamforming in user-centric network (UCN) is emerged, where the additional degrees of freedom of multi-antenna and cell coordination aggravate the challenges. This letter proposes a deep residual learning framework, UcnBeamNet, to enhance the ability of approximating the iterative algorithm for sum rate maximization, where multi-branch subnets are connected in parallel to extract extra information. Specifically, a weighted minimum mean square error (WMMSE)-based algorithm is derived to determine the optimal clusters and beamforming matrices; then UcnBeamNet is trained to learn the input-output mapping and provide direct insight of UCN from association matrices in addition to plural inputs. Extensive experiments demonstrate UcnBeamNet still reaches 90.38% sum-rate relative to conventional algorithm even with a large network size, and achieves more than 50, $000\times $ speed up in computational efficiency.

Hybrid Decode-Forward & Amplify-Forward Relaying With Non-Orthogonal Multiple Access

Abstract: Cooperative communication has used to be a hot topic and it has been studied extensively in the past 10 years, but in recent years, it becomes less likely to find substantial innovation in this field as before. In this paper, we propose a new hybrid decode-forward and amplify-forward with non-orthogonal multiple access (NOMA) (HDAF-NOMA) transmission scheme for a cellular system with multiple relays. To the best of our knowledge, this is the first work that attempts to integrate decode-forward (DF), amplify-forward, and NOMA into one strategy design to improve system performance. To verify the performance advantages, the proposed HDAF-NOMA scheme is compared with the other four traditional schemes in terms of channel capacity and average system throughput, and the optimal number of selected DF relays is also determined for the HDAF-NOMA scheme. Simulation results show that compared with the traditional schemes, the proposed HDAF-NOMA scheme can achieve larger sum channel capacity for the transmission of $x_{1}$ and $x_{2}$ , and it can also achieve larger average system throughput at high SNR region.

Flexible Coverage for Backhaul-Limited Ultradense Heterogeneous Networks: Throughput Analysis and $\eta$ -Optimal Biasing

Abstract: In fifth-generation ultradense heterogeneous network, backhaul plays an important role to provide connection to core networks for small base stations (SBSs). With the densification, nonideal backhaul will be deployed extensively, which constrains the throughput improvement due to the limited backhaul capacity. This paper models the SBSs and users as independent Poisson point processes and derives a semi-closed-form expression that analyzes the ergodic throughput of the network where SBSs have limited backhaul capacity. Based on analysis, to accomplish flexible coverage with limited backhaul, an optimization problem is formulated which accounts for cell association and aims at maximizing the number of offloading users while simultaneously minimizing the biasing factors and guaranteeing the received signal-to-interference-and-noise ratio of users when offloading. The optimization problem is shown to be a mixed-integer nonlinear problem, which can obtain a suboptimal solution by convex relaxation and further a distributed solution by problem decomposition via primal–dual method. Considering the complexity of ultradense network and practical implementation, a backhaul-aware $\eta$ -optimal biasing adjustment model is proposed for flexible coverage. This model aims to optimize the coverage for throughput improvement while matching the backhaul capacity. The simulation results show that average user and SBS throughput can be improved significantly by $2 \times$ when backhaul is constrained. Due to the transmit power and interference constraints on access link, there is no need to allocate too much bandwidth to backhaul, which provides guidance for self-backhaul resource allocation.

A Survey on Non-Orthogonal Multiple Access for 5G Networks: Research Challenges and Future Trends

Abstract: Non-orthogonal multiple access (NOMA) is an essential enabling technology for the fifth-generation (5G) wireless networks to meet the heterogeneous demands on low latency, high reliability, massive connectivity, improved fairness, and high throughput. The key idea behind NOMA is to serve multiple users in the same resource block, such as a time slot, subcarrier, or spreading code. The NOMA principle is a general framework, and several recently proposed 5G multiple access schemes can be viewed as special cases. This survey provides an overview of the latest NOMA research and innovations as well as their applications. Thereby, the papers published in this special issue are put into the context of the existing literature. Future research challenges regarding NOMA in 5G and beyond are also discussed.

A Survey on Non-Orthogonal Multiple Access for 5G Networks: Research Challenges and Future Trends

Abstract: Non-orthogonal multiple access (NOMA) is an essential enabling technology for the fifth generation (5G) wireless networks to meet the heterogeneous demands on low latency, high reliability, massive connectivity, improved fairness, and high throughput. The key idea behind NOMA is to serve multiple users in the same resource block, such as a time slot, subcarrier, or spreading code. The NOMA principle is a general framework, and several recently proposed 5G multiple access schemes can be viewed as special cases. This survey provides an overview of the latest NOMA research and innovations as well as their applications. Thereby, the papers published in this special issue are put into the content of the existing literature. Future research challenges regarding NOMA in 5G and beyond are also discussed.

A Survey of Non-Orthogonal Multiple Access for 5G

Abstract: In the fifth generation (5G) of wireless communication systems, hitherto unprecedented requirements are expected to be satisfied. As one of the promising techniques of addressing these challenges, non-orthogonal multiple access (NOMA) has been actively investigated in recent years. In contrast to the family of conventional orthogonal multiple access (OMA) schemes, the key distinguishing feature of NOMA is to support a higher number of users than the number of orthogonal resource slots with the aid of non-orthogonal resource allocation. This may be realized by the sophisticated inter-user interference cancellation at the cost of an increased receiver complexity. In this paper, we provide a comprehensive survey of the original birth, the most recent development, and the future research directions of NOMA. Specifically, the basic principle of NOMA will be introduced at first, with the comparison between NOMA and OMA especially from the perspective of information theory. Then, the prominent NOMA schemes are discussed by dividing them into two categories, namely, power-domain and code-domain NOMA. Their design principles and key features will be discussed in detail, and a systematic comparison of these NOMA schemes will be summarized in terms of their spectral efficiency, system performance, receiver complexity, etc. Finally, we will highlight a range of challenging open problems that should be solved for NOMA, along with corresponding opportunities and future research trends to address these challenges.

UAV Communications for 5G and Beyond: Recent Advances and Future Trends

Abstract: Providing ubiquitous connectivity to diverse device types is the key challenge for 5G and beyond 5G (B5G). Unmanned aerial vehicles (UAVs) are expected to be an important component of the upcoming wireless networks that can potentially facilitate wireless broadcast and support high rate transmissions. Compared to the communications with fixed infrastructure, UAV has salient attributes, such as flexible deployment, strong line-of-sight (LoS) connection links, and additional design degrees of freedom with the controlled mobility. In this paper, a comprehensive survey on UAV communication towards 5G/B5G wireless networks is presented. We first briefly introduce essential background and the space-air-ground integrated networks, as well as discuss related research challenges faced by the emerging integrated network architecture. We then provide an exhaustive review of various 5G techniques based on UAV platforms, which we categorize by different domains including physical layer, network layer, and joint communication, computing and caching. In addition, a great number of open research problems are outlined and identified as possible future research directions.